Biological fingerprints extracted from clinical images can be used for patient identity verification to determine misfiled clinical images in picture archiving and communication systems. However, such
1. Morishita J, Ueda Y: New solutions for automated image recognition and identification: challenges to radiologic technology
and forensic pathology. Radiol Phys Technol 14:123-133, 2021.
https://doi.org/10.1007/s12194-021-00611-9.
2. Morishita J, Katsuragawa S, Kondo K, Doi K: An automated
patient recognition method based on an image-matching technique
using previous chest radiographs in the picture archiving and communication system environment. Med Phys 28(6):1093–1097,
2001. https://doi.org/10.1118/1.1373403
3. Morishita J, Watanabe H, Katsuragawa S, et al.: Investigation of
misfiled cases in the PACS environment and a solution to prevent
18. 19. 20. filing errors for chest radiographs. Acad Radiol 12(1):97-103,
2005. https://doi.org/10.1016/j.acra.2004.11.008.
Bittle MJ, Charache P, Wassilchalk DM: Registration-associated
patient misidentification in an academic medical center: causes
and corrections. Jt Comm J Qual Patient Saf 33(1):25-33, 2007.
https://doi.org/10.1016/S1553-7250(07)33004-3
Danaher LA, Howells J, Holmes P, Scally P: Is it possible to eliminate
patient identification errors in medical imaging? J Am Coll Radiol
8(8):568-574, 2011. https://doi.org/10.1016/j.jacr.2011.02.021.
Seiden SC, Barach P. Wrong-side/wrong-site, wrong-procedure,
and wrong-patient adverse events: Are they preventable? Arch Surg
141(9):931-939, 2006. https://doi.org/10.1001/archsurg.141.9.931
Henneman PL, Fisher DL, Henneman EA, Pham TA, Campbell
MM, Nathanson BH: Patient identification errors are common in
a simulated setting. Ann Emerg Med 55(6):503-509, 2010. https://
doi.org/10.1016/j.annemergmed.2009.11.017.
Schulmeister L: Patient misidentification in oncology care. Clin
J Oncol Nurs 12(3):495-498, 2008. https://doi.org/10.1188/08.
CJON.495-498.
The Joint Commission: National Patient Safety Goals Effective
July 2020 for the Hospital Program. 2020. Available at https://
www.j ointc ommis sion.o rg/-/m edia/t jc/d ocume nts/standa rds/
national-patient-safety-goals/2020/npsg_chapter_hap_jul2020.
pdf. Accessed Sep 28, 2021.
Emergency Care Research Institute: Patient identification errors.
2016. Available at https://www.ecri.org/Resources/HIT/Patient%
20ID/Patient_Identification_Evidence_Based_Literature_final.
pdf. Accessed Sep 28, 2021.
Healthcare Financial Management Association. 2016. The Value
of Precise Patient Identification. Accessed Sep 28, 2021. https://
www.imprivata.com/resources/whitepapers/hfma-educational-
report-value-precise-patient-identification.
Packhäuser K, Gündel S, Münster N, et al.: Deep learning-based
patient re-identification is able to exploit the biometric nature of
medical chest X-ray data. Sci Rep 12:14851, 2022. https://d oi.o rg/
10.1038/s41598-022-19045-3
Ueda Y, Morishita J, Kudomi S, Ueda K: Usefulness of biological
fingerprint in magnetic resonance imaging for patient verification.
Med Biol Eng Comput 54:1341-1351, 2016. https://doi.org/10.
1007/s11517-015-1380-x.
Ueda Y, Morishita J, Hongyo T: Biological fingerprint using scout
computed tomographic images for positive patient identification.
Med Phys 46:4600-4609, 2019. https://d oi.o rg/1 0.1 002/m
p.1 3779.
Ueda Y, Morishita J, Kudomi S: Biological fingerprint for patient
verification using trunk scout views at various scan ranges in
computed tomography. Radiol Phys Technol 15: 398-408, 2022.
https://doi.org/10.1007/s12194-022-00682-2
Morishita J, Katsuragawa S, Sasaki Y, Doi K: Potential usefulness of biological fingerprints in chest radiographs for automated
patient recognition and identification. Acad Radiol 11:309–315,
2004. https://doi.org/10.1016/s1076-6332(03)00655-x.
Shimizu Y, Matsunobu Y, Morishita J: Evaluation of the usefulness of modified biological fingerprints in chest radiographs for
patient recognition and identification. Radiol Phys Technol 9:240244, 2016. https://doi.org/10.1007/s12194-016-0355-4.
Shimizu Y, Morishita J: Development of a method of automated
extraction of biological fingerprints from chest radiographs as
preprocessing of patient recognition and identification. Radiol
Phys Technol 10:376-381, 2017. https:// d oi. o rg/ 1 0. 1 007/
s12194-017-0400-y.
Kao EF, Lin WC, Jaw TS, Liu GC, Wu JS, Lee CN: Automated patient
identity recognition by analysis of chest radiograph features. Acad
Radiol 20:1024-1031, 2013. https://doi.org/10.1016/j.acra.2013.04.006.
Shamir L, Ling S, Rahimi S, Ferrucci L, Goldberg IG: Biometric identification using knee X-rays. Int J Biom 1:365-370, 2009.
https://doi.org/10.1504/IJBM.2009.024279.
13
21. Lamb JM, Agazaryan N, Low DA: Automated patient identification and localization error detection using 2-dimensional to
3-dimensional registration of kilovoltage x-ray setup images. Int
J Radiat Oncol Biol Phys 87:390-393, 2013. https://doi.org/10.
1016/j.ijrobp.2013.05.021.
22. Silverstein E, Snyder M: Implementation of facial recognition
with Microsoft Kinect v2 sensor for patient verification. Med Phys
44:2391-2399. 2017. https://doi.org/10.1002/mp.12241
23. Wiant DB, Verchick Q, Gates P, et al.: A novel method for radiotherapy patient identification using surface imaging. J Appl Clin Med
Phys 17:271-278, 2016. https://doi.org/10.1120/jacmp.v17i2.6066
24. Parks CL, Monson KL: Automated facial recognition of computed tomography-derived facial images: patient privacy implications. J Digit Imaging 30:204-214, 2017. https://doi.org/10.1007/
s10278-016-9932-7
25. Koike-Akino T, Mahajan R, Marks TK, et al.: High-accuracy user
identification using EEG biometrics. Conf Proc IEEE Eng Med
Biol Soc 2016: 854-858, 2016.
26. Toge R, Morishita J, Sasaki Y, Doi K: Computerized image-searching
method for finding correct patients for misfiled chest radiographs in a
PACS server by use of biological fingerprints. Radiol Phys Technol
6:437–443, 2013. https://doi.org/10.1007/s12194-013-0221-6
27. Sakai Y, Takahashi K, Shimizu Y, Ishibashi E, Kato T, Morishita J:
Clinical application of biological fingerprints extracted from averaged
chest radiographs and template-matching technique for preventing
left-right flipping mistakes in chest radiography. Radiol Phys Technol
12:216-223, 2019. https://doi.org/10.1007/s12194-019-00504-y
28. Nguyen K, Nguyen HH, Tiulpin A: AdaTriplet: Adaptive gradient
triplet loss with automatic margin learning for forensic medical image
matching. In: Wang L, Dou Q, Fletcher PT, Speidel S, Li S (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI
2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13438,
725–735, 2022. https://doi.org/10.1007/978-3-031-16452-1_69
29. Ciaffi R, Gibelli D, Cattaneo C: Forensic radiology and personal identification of unidentified bodies: a review. Radiol Med
116:960-968, 2011. https://doi.org/10.1007/s11547-011-0677-6
30. Clemente MA, La Tegola L, Mattera M, Guglielmi G: Forensic
radiology: An update. J Belg Soc Radiol 101(Suppl 2):21, 2017.
https://doi.org/10.5334/jbr-btr.1420
31. Matsunobu Y, Morishita J, Usumoto Y, Okumura M, Ikeda N:
Bone comparison identification method based on chest computed
tomography imaging. Leg Med (Tokyo) 29:1-5, 2017. https://doi.
org/10.1016/j.legalmed.2017.08.002
32. Wada Y, Morishita J, Yoon Y, Okumura M, Ikeda N: A simple
method for the automatic classification of body parts and detection
of implanted metal using postmortem computed tomography scout
view. Radiol Phys Technol 13:378-384, 2020. https://doi.org/10.
1007/s12194-020-00581-4
33. Kawazoe Y, Morishita J, Matsunobu Y, Okumura M, Shin S, Usumoto
Y, Ikeda N: A simple method for semi-automatic readjustment for
positioning in post-mortem head computed tomography imaging. J
Forensic Radiol Imaging 16:57–64, 2019. https://doi.org/10.1016/j.
jofri.2019.01.004
34. Krishan K, Chatterjee PM, Kanchan T, Kaur S, Baryah N, Singh RK:
A review of sex estimation techniques during examination of skeletal
remains in forensic anthropology casework. Forensic Sci Int 261:165.
e1-165.e1658, 2016. https://doi.org/10.1016/j.forsciint.2016.02.007
35. Tsubaki S, Morishita J, Usumoto Y, et al.: Sex determination
based on a thoracic vertebra and ribs evaluation using clinical
chest radiography. Leg Med (Tokyo) 27:19-24, 2017. https://doi.
org/10.1016/j.legalmed.2017.06.003
36. Kim TK, Yi PH, Wei J et al.: Deep learning method for automated classification of anteroposterior and posteroanterior chest
radiographs. J Digit Imaging 32:925-930, 2019. https://doi.org/
10.1007/s10278-019-00208-0
13
Journal of Digital Imaging
37. Bae J, Yu S, Oh J, Kim TH, Chung JH, Byun H, Yoon MS, Ahn C,
Lee DK: External validation of deep learning algorithm for detecting and visualizing femoral neck fracture including displaced and
non-displaced fracture on plain X-ray. J Digit Imaging. 34:10991109, 2021. https://doi.org/10.1007/s10278-021-00499-2
38. Suzuki T, Maki S, Yamazaki T, Wakita H, Toguchi Y, Horii M,
Yamauchi T, Kawamura K, Aramomi M, Sugiyama H, Matsuura
Y, Yamashita T, Orita S, Ohtori S: Detecting distal radial fractures from wrist radiographs using a deep convolutional neural
network with an accuracy comparable to hand orthopedic surgeons. J Digit Imaging. 35:39-46, 2022. https://doi.org/10.1007/
s10278-021-00519-1
39. Liu F, Gao L, Wan J, Lyu ZL, Huang YY, Liu C, Han M: Recognition of digital dental X-ray images using a convolutional neural
network. J Digit Imaging. 36:73-79, 2023. https://d oi.o rg/1 0.1 007/
s10278-022-00694-9.
40. Çallı E, Sogancioglu E, van Ginneken B, van Leeuwen KG, Murphy
K: Deep learning for chest X-ray analysis: A survey. Med Image
Anal. 72:102125, 2021. https://doi.org/10.1016/j.media.2021.
102125
41. Agrawal T, Choudhary P: Segmentation and classification on chest
radiography: a systematic survey. Vis Comput. 39:875-913, 2023.
https://doi.org/10.1007/s00371-021-02352-7
42. Baltruschat IM, Nickisch H, Grass M et al.: Comparison of deep
learning approaches for multi-label chest X-ray classification. Sci
Rep 9: 6381, 2019. https://doi.org/10.1038/s41598-019-42294-8
43. Ieki H, Ito K, Saji M et al.: Deep learning-based age estimation
from chest X-rays indicates cardiovascular prognosis. Commun Med (Lond) 2:159, 2022. https://doi.org/10.1038/s43856-
022-00220-6
44. Gichoya JW, Banerjee I, Bhimireddy AR et al.: AI recognition
of patient race in medical imaging: a modelling study. Lancet
Digit Health. 4:e406-e414, 2022. https://doi.org/10.1016/S2589-
7500(22)00063-2.
45. Yang M, Tanaka H, Ishida T: Performance improvement in multilabel thoracic abnormality classification of chest X-rays with
noisy labels. Int J Comput Assist Radiol Surg 18(1):181-189,
2022. https://doi.org/10.1007/s11548-022-02684-2.
46. Kawakubo M, Waki H, Shirasaka T, Kojima T, Mikayama R,
Hamasaki H, Akamine H, Kato T, Baba S, Ushiro S, Ishigami
K: A deep learning model based on fusion images of chest radiography and X-ray sponge images supports human visual characteristics of retained surgical items detection. Int J Comput Assist
Radiol Surg 2022. https://doi.org/10.1007/s11548-022-02816-8.
47. Wu C, Wu H, Lei S, Li X, Tong H: Deep learning in automatic fingerprint identification. 2021 IEEE 6th International Conference on Smart
Cloud. 111–116, 2021. https://doi.org/10.1109/SmartCloud52277.
2021.00027
48. Finizola J, Targino J, Teodoro F, Lima C: Comparative study
between deep face, autoencoder and traditional machine learning
techniques aiming at biometric facial recognition. 2019 International Joint Conference on Neural Networks (IJCNN) 1–8, 2022.
https://doi.org/10.1109/IJCNN.2019.8852273
49. Priyadharshini AR, Arivazhagan S, Arun M: A deep learning
approach for person identification using ear biometrics. Appl
Intell (Dordr). 51:2161–2172, 2021. https://doi.org/10.1007/
s10489-020-01995-8
50. Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM: ChestXRay8: Hospital-scale chest X-ray database and benchmarks on
weakly-supervised classification and localization of common
thorax diseases. 2017 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), Honolulu, HI, USA, 3462–3471,
2017. https://doi.org/10.1109/CVPR.2017.369
51. Bustos A, Pertusa A, Salinas JM, de la Iglesia-Vayá M: PadChest:
A large chest x-ray image dataset with multi-label annotated
Journal of Digital Imaging 52. 53. 54. 55. 56. 57. reports. Med Image Anal 66, 101797, 2020. https://doi.org/10.
1016/j.media.2020.101797
Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C,
Marklund H, Haghgoo B, Ball R, Shpanskaya K, Seekins J. CheXpert: A large chest radiograph dataset with uncertainty labels and
expert comparison. In Proceedings of the AAAI Conference on
Artificial Intelligence 33:590-597, 2019. https://doi.org/10.1609/
aaai.v33i01.3301590
Kwon J, Kim J, Park H, Choi IK: Asam: Adaptive sharpnessaware minimization for scale-invariant learning of deep neural
networks. In Proceedings of the 38th International Conference on
Machine Learning. PMLR. 2021. https://proceedings.mlr.press/
v139/kwon21b.html.
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC.: Mobilenetv2: Inverted residuals and linear bottlenecks. 2018 IEEE/CVF
Conference on Computer Vision and Pattern Recognition (CVPR)
4510–4520, 2018. https://doi.org/10.1109/CVPR.2018.00474
Tan M, Chen B, Pang R, et al.: MnasNet: Platform-aware neural
architecture search for mobile. 2019 IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR). 2815–2823,
2019. https://doi.org/10.1109/CVPR.2019.00293
Tan M, Le Q: EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International
Conference on Machine Learning, ICML 2019. 6105–6114, 2019.
Tan M, Le Q: EfficientNetV2: Smaller models and faster training. Proceedings of the 38th International Conference on Machine
Learning, PMLR 139:10096–10106, 2021.
58. Musgrave K, Belongie S, Lim SN: A metric learning reality check.
In: Vedaldi A, Bischof H, Brox T, Frahm JM. (eds) Computer
Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer
Science, 12370. 681–699, Springer, Cham. 2020. https://doi.org/
10.1007/978-3-030-58595-2_41
59. Zhang X, Zhao R, Qiao Y, Wang X, Li H: AdaCos: Adaptively
scaling cosine logits for effectively learning deep face representations. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 10815–10824, 2019. https://d oi.o rg/1 0.
1109/CVPR.2019.01108
60. Nguyen HV, Bai L: Cosine similarity metric learning for face
verification. In: Kimmel R, Klette R, Sugimoto A. (eds) Computer
Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer
Science, vol 6493. 709–720, https://doi.org/10.1007/978-3-642-
19309-5_55
61. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the
areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 44:837-845,
1988. https://doi.org/10.2307/2531595
62. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D:
Grad-CAM: visual explanations from deep networks via gradientbased localization. Int J Comput Vis 128:336-359, 2020. https://doi.
org/10.1007/s11263-019-01228-7
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